The study aims to provide information on efficiency opportunities on SCA's northbound cassettes. It has been made by examining the capacity utilization rate on the northbound cassettes on SCA's vessels for a period of two weeks. The cargo loaded in the ports of Rotterdam and Sheerness consists of external cargo of varying shape. The cargo is shipped northbound to Holmsund and Sundsvall. Measurements have been made on the cargo to the final destinations Sundsvall, Holmsund and Finland. The measurements have been used in a mathematical optimization model created to optimize the loading of the cassettes. The model is based on placing boxes in a grid where the boxes that are placed representing the cargo and the grids representing the cassettes. The aim of the model is to reduce the number of cassettes and thereby increase the capacity utilization rate. The study resulted in an increase in capacity utilization rate for both area and volume to all destinations. The overall improvement for all cassettes examined resulted in an increase in the area capacity utilization rate by 9.02 percentage points and 5.72 percentage points for the volume capacity utilization rate. It also resulted in a decrease of 22 cassettes in total on the four voyages that were examined which indicate that there are opportunities to improve the capacity utilization rate. The study also shows that the model can be used as a basis for similar problems.

In recent years, the importance of incorporating uncertainty into planning models for logistics and transportation systems has been widely recognized in the Operations Research and transportation science communities. Maritime transportation, as a major mode of transport in the world, is subject to a wide range of disruptions at the strategic, tactical and operational levels. This thesis is mainly concerned with the development of robustness planning strategies that can mitigate the effects of some major types of disruptions for an important class of optimization problems in the shipping industry. Such problems arise in the creation and negotiation of long-term delivery contracts with customers who require on-time deliveries of high-value goods throughout the year. In this thesis, we consider the disruptions that can increase travel times between ports and ultimately affect one or more scheduled deliveries to the customers. Computational results show that our integrated solution procedure and robustness planning strategies can generate delivery plans that are both economical as well as robust against uncertain disruptions.

Most traditional routing problems assume perfect operability of all arcs and nodes. However, when independent arc failure probabilities exist, a secondary objective must be present to retain some measure of expected functionality. We first briefly consider the reliability-constrained single-path problem, where we look for the lowest cost path that meets a reliability side constraint. This analysis enables us to then examine the reliability-constrained two-path problem, which seeks to establish two minimum-cost paths between a source and destination node wherein at least one path must remain fully operable with some threshold probability. We consider the case in which both paths must be arc-disjoint and the case in which arcs can be shared between the paths. We prove both problems to be NP-hard. We examine strategies for solving the resulting nonlinear integer program, including pruning, coefficient tightening, lifting, and branch-and-bound partitioning schemes. Next, we consider the reliable h-path routing problem, which seeks a minimum-cost set of h ≥ 2 arc-independent paths between a source and destination node, such that the probability that at least one path remains operational is sufficiently large. Our prior arc-based models and algorithms tailored for the case in which h = 2 do not extend well to the general h-path problem. Thus, we propose two alternative integer programming formulations for the h-path problem in which the variables correspond to origin-destination paths. We propose two branch-and-price-and-cut algorithms for solving these new formulations, and provide computational results to demonstrate the efficiency of these algorithms. Finally, we examine the robust design of an evacuation tree, in which evacuation is subject to capacity restrictions on arcs. Given a discrete set of disaster scenarios with varying network populations, arc capacities, transit times, and time-dependent penalty functions, we seek to establish an optimal a priori evacuation tree that minimizes the expected evacuation penalty. The solution strategy is based on Benders decomposition, and we provide effcient methods for obtaining primal and dual sub-problem solutions. We analyze techniques for strengthening the master problem formulation, thus reducing the number of master problem solutions required for the algorithm's convergence.

This research deals with the optimization of metrology and calibration problems. The optimization involved here is the application scientifically sound operations research techniques to help in solving the problem intended optimally or semi-optimally with a practical time frame. The research starts by exploring the subject of measurement science known as metrology. This involves defining all the constituents of metrology facilities along with their various components. The definitions include the SI units’ history and structure as well as their characteristics. After that, a comprehensive description of most of the operations and parameters encountered in metrology is presented. This involves all sources of uncertainties in most of the parameters that affect the measurements. From the background presented and using all the information within it; an identification of the most important and critical general problems is attempted. In this treatment a number of potential optimization problems are identified along with their description, problem statement definition, impact on the system and possible treatment method. After that, a detailed treatment of the scheduling problem, the calibration interval determination problem and the average outgoing quality problem is presented. The scheduling problem is formulated and modelled as a mixed integer program then solved using LINGO program. A heuristic algorithm is then developed to solve the problem near optimally but in much quicker time, and solution is packaged in a computer program. The calibration interval problem treatment deals with the determination of the optimal CI. Four methods are developed to deal with different cases. The cases considered are the reliability target case, the CI with call cost and failure cost of both first failure and all failures and the case of large number of similar TMDEs. The average out going quality (AOQ) treatment involves the development two methods to assess the AOQ of a calibration facility that uses a certain multistage inspection policy. The two methods are mathematically derived and verified using a simulation model that compares them with an actual failure rate of a virtual calibration facility.

The transmission of power across the GB transmission system, as operated by National Grid, results in inevitable loss of electrical power. Operationally these power losses cannot be eliminated, but they can be reduced by adjustment of the system voltage profile. At present the minimisation of active power losses relies upon a lengthy manually based iterative adjustment process. Therefore the system operator requires the development of advanced optimisation tools to cope with the challenges faced over the next decade, such as achieving the stringent greenhouse gas emission targets laid down by the UK government, while continue to provide an economical, secure and efficient service. To meet these challenges the research presented in this thesis has developed optimisation techniques that can assist control centre engineers by automatically setting up voltage studies that are low loss and low cost. The proposed voltage optimisation techniques have been shown to produce solutions that are secured against 800 credible contingency cases. A prototype voltage optimisation tool has been deployed, which required the development of a series of novel approaches to extend the functionality of an existing optimisation program. This research has lead to the development of novel methods for handling multi-objectives, contradictory shunt switching configurations and selecting all credible contingencies. Studies indicate that a theoretical loss saving of 1.9% is achievable, equivalent to an annual emissions saving of approximately 64,000 tonnes of carbon dioxide. A novel security constrained mixed integer non-linear optimisation technique has also been developed. The proposed method has been shown to be superior to several conventional methods on a wide range of IEEE standard network models and also on a range of large-scale GB network models. The proposed method manages to further reduce active power losses and also satisfies all security constraints.

Sugar mills represent significant capital investments. To maintain appropriate returns on their investment, sugar companies seek to run the mills at capacity over the sugarcane harvest season. Because the sugar content of cane degrades considerably once it is cut, maintaining inventories of cut cane is undesirable. Instead, mills want to coordinate the arrival of cut cane with production. We present exact solution approaches exploiting special structure of the sugarcane harvest logistics problem in Brazil and the United States.

The United State’s renewable portfolio standards call for a large increase of renewable energy and improved conservation efforts over today’s current system. Wind will play a ma jor role in meeting the renewable portfolio standards. As a result, the amount of wind capacity and generation has been growing exponentially over the past 10 to 15 years. The proposed unit commitment method integrates wind energy into a scheduable resource while keeping the formulation simple using mixed integer programming. A reserve constraint is developed and added to unit commitment giving the forecasted wind energy an effective cost. The reserve constraint can be scaled based on the needs of the system: cost, reliability, or the penetration of wind energy. The results show that approximately 24% of the load can be met in the given test system, while keeping a constant reliability before and after wind is introduced. This amount of wind will alone meet many of the renewable portfolio standards in the United States.

This study addresses the problem of designing a new natural gas transmission network or expanding an existing network while minimizing the total investment and operating costs. A substantial reduction in costs can be obtained by effectively designing and operating the network. A well-designed network helps natural gas companies minimize the costs while increasing the customer service level. The aim of the study is to determine the optimum installation scheduling and locations of new pipelines and compressor stations. On an existing network, the model also optimizes the total flow through pipelines that satisfy demand to determine the best purchase amount of gas. A mixed integer nonlinear programming model for steady-state natural gas transmission problem on tree-structured network is introduced. The problem is a multi-period model, so changes in the network over a planning horizon can be observed and decisions can be made accordingly in advance. The problem is modeled and solved with easily accessible modeling and solving tools in order to help decision makers to make appropriate decisions in a short time. Various test instances are generated, including problems with different sizes, period lengths and cost parameters, to evaluate the performance and reliability of the model. Test results revealed that the proposed model helps to determine the optimum number of periods in a planning horizon and the crucial cost parameters that affect the network structure the most.

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The petrochemical Industry is based upon the production of chemicals from petroleum and also deals with chemicals manufactured from the by products of petroleum refinery. At the preliminary stages of chemical plant development and design, the choice of chemical process route is the key design decision. In the past, economics were the most important criterion in choosing the chemical process route. Modified studies imply that the two of the important planning objectives for a petrochemical industry, environmental risk and the industrial safety involved in the development. For the economic evaluation of the industry, and for the proposed final chemicals products in the development, simple and clear economic indicators are needed to be able to indicate an overall economic gain in the development. Safety, as the second objective, is considered in this study as the risk of chemical plant accidents. Risk, when used as an objective function, has to have a simple quantitative form to be easily evaluated for a large number of possible plants in the petrochemical network. The simple quantitative form adopted is a safety index that enables the number of people affected by accidents resulting in chemical releases to be estimated. Environmental issues have now become important considerations due to the potential harmful impacts produced by chemical releases. In this study third objective of planning petrochemical industry was developed by involving environmental considerations and environmental risk index. Indiana Relative Chemical Hazard Score (IRCHS) was used to allow chemical industries routes to be ranked by environmental hazardous. The focus of this work is to perform early planning and decision-making for a petrochemical plants network for maximum economical gain, minimum risk to people from possible chemical accidents and minimum environmental risk. The three objectives, when combined with constraints describing the desired or the possible structure of the industry, will form an optimization model. For this study, the petrochemical planning model consists of a Mixed Integer Linear Programming (MILP) model to select the best routes from the basic feedstocks available in Kuwait -as a case study- to the desired final products with multiple objective functions. The economic, safety and environmental risk objectives usually have conflicting needs. The presence of several conflicting objectives is typical when planning. In many cases, where optimization techniques are utilized, the multiple objectives are simply aggregated into one single objective function. Optimization is then conducted to get one optimal result. This study, which is concerned with economic and risk objectives, leads to the identification of important factors that affecting the building-up of environmental management system for petrochemical industry. Moreover, the procedure of modelling and model solution can be used to simplify the decision-making for complex or large systems such as the petrochemical industry. It presents the use of simple multiple objective optimization tools within a petrochemical planning tool formulated as a mixed integer linear programming model. Such a tool is particularly useful when the decision-making task must be discussed and approved by officials who often have little experience with optimization theories